Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 591:

  You are evaluating a completed binary classification machine learning model.

  You need to use the precision as the valuation metric.

  Which visualization should you use?

  A. Binary classification confusion matrix
  B. box plot
  C. Gradient descent
  D. coefficient of determination
  A. Binary classification confusion matrix

  ---

  Explanation

  https://machinelearningknowledge.ai/confusion-matrix-and-performance-metrics-machine-learning/

• Question 592:

  You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are removed.

  Which three Azure Machine Learning Studio modules should you use? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. Create Scatterplot
  B. Summarize Data
  C. Clip Values
  D. Replace Discrete Values
  E. Build Counting Transform
  A. Create Scatterplot
  B. Summarize Data
  C. Clip Values

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  Explanation

  B: To have a global view, the summarize data module can be used. Add the module and connect it to the data set that needs to be visualized.

  A: One way to quickly identify Outliers visually is to create scatter plots.

  C: The easiest way to treat the outliers in Azure ML is to use the Clip Values module. It can identify and optionally replace data values that are above or below a specified threshold.

  You can use the Clip Values module in Azure Machine Learning Studio, to identify and optionally replace data values that are above or below a specified threshold. This is useful when you want to remove outliers or replace them with a mean, a constant, or other substitute value.

  References:

  https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clip-values

  Question Set 3

• Question 593:

  DRAG DROP

  You are designing an Azure Machine Leaning solution by using the Python SDK v2.

  You must train and deploy the solution by using a compute target.

  The compute target must meet the following requirements:

  1. Enable the use of on-premises compute resources.

  2. Support autoscalling.

  You need to configure a compute target for training and inference.

  Which compute target t should you configure?

  To answer select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  

• Question 594:

  You have the following Azure subscriptions and Azure Machine Learning service workspaces:

  

  You need to obtain a reference to the ml-project workspace.

  Solution: Run the following Python code:

  

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

• Question 595:

  HOTSPOT

  You create an Azure Machine Learning workspace.

  You need to detect data drift between a baseline dataset and a subsequent target dataset by using the DataDriftDetector class.

  How should you complete the code segment? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

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  Box 1: create_from_datasets

  The create_from_datasets method creates a new DataDriftDetector object from a baseline tabular dataset and a target time series dataset.

  Box 2: backfill

  The backfill method runs a backfill job over a given specified start and end date.

  Syntax: backfill(start_date, end_date, compute_target=None, create_compute_target=False)

  Incorrect Answers:

  List and update do not have datetime parameters.

  https://docs.microsoft.com/en-us/python/api/azureml-datadrift/azureml.datadrift.datadriftdetector(class)

• Question 596:

  You create an Azure Machine Learning workspace.

  You must configure an event handler to send an email notification wten data drift is detected in the workspace datasets. You must minimize development efforts.

  You need to configure an Azure service to send the notification.

  Which Azure service should you use?

  A. Azure Function apps
  B. Azure DevOps pipeline
  C. Azure Automation runbook
  D. Azure Logic Apps
  D. Azure Logic Apps

• Question 597:

  You manage an Azure Machine Learning workspace.

  You build an Azure Machine Learning pipeline for image classification by using custom components.

  You need to define the interface, metadata, and execution logic from a Python function.

  Which function should you use?

  A. create_or_update()
  B. load_component()
  C. from_config()
  D. command_component()
  D. command_component()

• Question 598:

  HOTSPOT

  You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).

  The remaining 1,000 rows represent class 1 (10 percent).

  The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.

  You need to configure the module.

  Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: 300

  You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).

  Box 2: 5

  We should use 5 data rows.

  Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.

  By increasing the number of nearest neighbors, you get features from more cases.

  By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

• Question 599:

  You manage an Azure Machine Learning workspace. You develop a regression model training pipeline by using notebooks.

  You need to determine the appropriate evaluation metrics for the experiment.

  Which two metrics should you choose? (Choose two.) Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Precision
  B. Correlation
  C. Recall
  D. Residuals
  B. Correlation
  D. Residuals

• Question 600:

  DRAG DROP

  You manage an Azure Machine Learning workspace named workspaces

  You plan to create a registry named registry01 with the help of the following registry.yml (line numbers are used for reference only):

  

  You need to use Azure Machine Learning Python SDK v2 with Python 3.10 in a notebook to interact with workspace1.

  Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

  Select and Place: